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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20484 条记录，以下是361-370 订阅

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Automated Detection of Poultry Diseases using Implicit Pattern Mining over Explicit image Analysis

Automated Detection of Poultry Diseases using Implicit Patte...

引用

2024 ieee international conference on signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Donthi, Khushi Kiran, Kuval Srinivasa, Gowri PES University PES Center for Pattern Recognition Dept. of CSE Bangalore India

ISBN: (纸本)9798350376135

Early detection of poultry diseases is crucial for poultry farmers, but traditional identification methods are often slow and tedious due to limited facilities. We present a comprehensive evaluation of implicit pattern mining techniques using lightweight, CNN-based deep learning models such as, MobileNetV2, InceptionV3, and EfficientNetB5 that are amenable for implementation on edge devices. We contrast them with traditional machine learning approaches applied to analyse chicken fecal image data to classify images of healthy samples from unhealthy ones. We use model interpretation to gain insight to the patterns in the input image that the deep learning models use to make predictions. This facilitates corroborating domain understanding with model performance. This study demonstrates the viability of deploying deep learning based methods for poultry disease detection on handheld devices, highlighting its potential for deployment in the field. © 2024 ieee.

关键词： Adversarial machine learning

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MetaCon: Revitalizing internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 ieee.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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ROBUST RGB-T TRACKING VIA CONSISTENCY REGULATED SCENE PERCEPTION 30

ROBUST RGB-T TRACKING VIA CONSISTENCY REGULATED SCENE PERCEP...

引用

30th ieee international conference on image Processing (ICIP)

作者： Kang, Bin Liu, Liwei Zhao, Shihao Du, Songlin Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China Southeast Univ Sch Automat Nanjing Peoples R China

ISBN: (纸本)9781728198354

RGB-T tracking has received increasing attention due to its significant advantage under severe weather conditions. Existing RGB-T tracking methods pay close attention to the representation of target appearance, ignoring the importance of scene information. In this paper, we propose a global reasoning-oriented method for RGB-T tracking. In particular, within a multi-task learning framework, our approach adopts a nested global reasoning model to regulate the consistency of scene perception (reasoning the relation between targets and the surrounding semantic regions) in different image domains. Moreover, a meta-unsupervised learning strategy is designed to enforce the nested global reasoning model to utilize partial multi-domain target information for the updating of scene perception. Extensive experiments on GTOT, RGBT210 and LasHeR datasets show the superior performance of our method when compared with related works.

关键词： RGB-T tracking Graph-based global reasoning Self-supervised learning

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Multi-Source Remote Sensing image Registration Method based on Nonlinear Scale Space and Phase Congruency 2

Multi-Source Remote Sensing Image Registration Method Based ...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Chen, Yuejuan Li, Xianglei Huang, Pingping Tan, Weixian Li, Xiujuan Yin, Bo Inner Mongolia University of Technology Mongolia Key Laboratory of Radar Technology and Application College of Information Engineering Hohhot China Inner Mongolia University of Technology Mongolia Key Laboratory of Radar Technology and Application College of Resources and Environmental Engineering Hohhot China

ISBN: (纸本)9798331515669

Due to differences in sensor characteristics, imaging conditions, and time among multi-source remote sensing images, nonlinear changes in image radiance intensity occur, increasing the difficulty of image registration. Existing techniques often face challenges in the robustness and accuracy of feature matching when addressing these nonlinear radiance variations. To solve this problem, this paper proposes a phase congruency remote sensing image registration method based on nonlinear scale space. This method constructs a nonlinear scale space of the image using an improved anisotropic diffusion filter, effectively suppressing noise while preserving the edges and detailed features of the image. It calculates the edge strength and corner strength of the image at different scales using a phase congruency model and extracts feature points from the edge strength map and corner strength map. Multi-scale, multi-direction descriptors are constructed using the response values generated by the Log-Gabor filter to enhance the robustness of feature extraction, thereby achieving precise registration of multi-source remote sensing images. Experimental results show that this method significantly suppresses image noise and nonlinear radiance differences, and outperforms traditional algorithms and other improved registration algorithms regarding the number of correctly matched point pairs and robustness. © 2024 ieee.

关键词： Gabor filters

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An Edge Filtering-based Spatial-Spectral Joint Hyperspectral Target-Level Anomaly Detection 2

An Edge Filtering-Based Spatial-Spectral Joint Hyperspectral...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Wang, Zihan Nie, Cong Wang, Wenzheng Zhang, Genrui Beijing Institute of Technology Radar Technology Research Institute Beijing China Xi'an Modern Control Technology Research Institute Xi'an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515669

Hyperspectral images have demonstrated exceptional performance in anomaly detection due to the strong distinctiveness of the spectral information they contain across different types of surfaces, drawing significant attention in applications such as civilian rescue operations and military search missions. However, commonly used hyperspectral anomaly detection techniques currently suffer from two major drawbacks: 1) existing anomaly detection algorithms overly focus on the spectral features of hyperspectral data while neglecting the spatial features contained within the image;2) current detection algorithms typically operate at the pixel level, lacking relevant research aimed at achieving precise target set annotation. To address these two issues, this paper proposes an edge and keypoint detection-based hyperspectral image target set anomaly detection algorithm. First, a hyperspectral image edge enhancement operator based on the Scharr operator is designed to highlight target edge information by calculating the spectral similarity between pixels. Next, a spectral boundary-keypoint generation algorithm is proposed, which determines the coordinates of the target edge extrema by detecting edges in four directions. Finally, a target box generation algorithm is developed, combining boundary keypoints with anomaly detection results, and the results are optimized using Non-Maximum Suppression (NMS) by traversing combinations of extreme points. Extensive qualitative and quantitative experiments demonstrate that the proposed framework significantly improves the Intersection over Union (IoU) between the generated target boxes and the ground truth boxes while reducing the center offset, compared to state-of-the-art methods. © 2024 ieee.

关键词： image annotation

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Channel Water Depth Warning System based on Digital image Processing and Swarm Intelligence Perception

Channel Water Depth Warning System Based on Digital Image Pr...

引用

2023 ieee international conference on Integrated Circuits and Communication systems, ICICACS 2023

作者： Xie, Hui Changjiang Luzhou Waterway Bureau Sichuan China

ISBN: (纸本)9798350398465

With the continuous development of waterway construction and the continuous expansion of the waterway system, more and more requirements are put forward for the safety of the waterway system. The channel water depth is an important part of the channel system. In order to obtain the crisis in the channel water depth in real time and accurately, it can improve the safety and economic benefits of ship navigation. The concept of real-time warning system of channel water depth is introduced. This paper mainly designs the model of the channel water depth warning system based on the transformation of digital image processing and swarm intelligence perception technology, and through the experimental test, the results show that the accurate early warning in the test of the channel water depth warning system based on image processing and swarm intelligence perception The accuracy rate reached 85.45%, and the accuracy rate of early warning during system training reached 79.56%. It can be proved that the system studied in this paper based on digital image processing and crowd perception has certain applicability in channel water depth warning. © 2023 ieee.

关键词： image processing

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A Novel End-to-End Framework to image Cortical Networks from EEG

A Novel End-to-End Framework to Image Cortical Networks from...

引用

ieee international conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications (CIVEMSA)

作者： Chen, Wanjun Wang, Junpu Yi, Chanlin Li, Fali Xu, Peng Univ Elect Sci & Technol China Sch Life Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9798350322996;9798350323009

Deciphering the intricate causal relationships among cortical sources in the brain's complex systems through a non-invasive and low-cost technique remains a formidable challenge. Electroencephalography (EEG), with its high temporal resolution, offers a promising avenue to unravel the underlying neural mechanisms. To this end, a novel end-to-end model is proposed to capture cortical networks from EEG data based on a temporal convolutional graph neural network (GNN), called TCGNN-BrainNet. The core components of this model are the inverse operator neural network (IONN) and the connectivity estimation neural network (CENN). The IONN transforms the EEG signal into an abstract representation of deep cortical activity, while the CENN constructs and leverages graph encoding for interaction inference. Through extensive training, TCGNN-BrainNet acquires the capability to infer cortical networks directly from EEG data, optimizing accuracy and mitigating error propagation common in conventional methods. Notably, the training strategy concurrently weaves the understanding of physiological cortical features with the implicit relational understanding of supervisory data, through the defined loss function. This enables the neural networks to organically uncover and seize subtle interaction patterns amidst the signals. Extensive simulations were performed to substantiate the efficacy of our model, evidencing the robustness and consistent reliability of TCGNN-BrainNet in accurately constructing cortical networks. These promising outcomes demonstrate the significant potential of TCGNN-BrainNet in practical brain network analyses, paving the way for new frontiers in neuroscientific exploration and advancements in the field.

关键词： cortical network causal inference graph neural network EEG

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Cluster Head Selection in IoT Networks Using Salp Swarm Optimization 1

Cluster Head Selection in IoT Networks Using Salp Swarm Opti...

引用

1st ieee international conference on Advances in signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Agrawal, Deepika Nit Raipur Information Technology Raipur India

ISBN: (纸本)9798350355093

Two major issues concerning handling internet of Things (IoT) networks are energy efficiency and efficient data transfer, especially in large-scale deployments. A popular technique to increase network efficiency is cluster-based routing protocols, in which data aggregation and communication are made easier by cluster heads (CHs). The lifespan and consumption of the energy in the network are directly impacted by the CHs that are chosen. Advanced methods for cluster head selection have been developed because of the increasing demand for reliable, scalable, and energy-efficient IoT networks. To maximize CH selection, the current investigation presents a unique cluster head selection technique based on Salp Swarm Optimization (SSO), which takes distance to the sink and remaining energy into account as fitness functions. When compared to conventional approaches, the proposed strategy shows substantial improvements in network lifespan and energy efficiency. © 2024 ieee.

关键词： CH Selection internet of Things (IoT) Salp Swarm Optimization Wireless Sensor Networks

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Automated Blood Group Detection System using image Processing for Non- Invasive Antigen Feature Extraction and Classification 3

Automated Blood Group Detection System using Image Processin...

引用

3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Reddy, Gopu Chandrasekhar SaiKrishna, Gajavada Annie Micheal, A. Department of CSE with AIML Sathyabama Institute of Science and Technology Chennai India Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods." © 2025 ieee.

关键词： Antigens

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Deep Learning-based Modulation Classification Leveraging Dual-Type image 15

Deep Learning-Based Modulation Classification Leveraging Dua...

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15th international conference on Information and Communication technology Convergence, ICTC 2024

作者： Cho, Yunseol Kim, Hanvit Park, Hyunwoo Park, Jiyeon Ji, Younggun Ju, Hyungjun Choi, Jaekark Im, Sanghun Kim, Kihun Kim, Sunwoo Hanyang University Department of Electronic Engineering Seoul Korea Republic of Tactical Communication System Team Hanwha Systems Seongnam Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we present an automatic modulation classification (AMC) algorithm for identifying overlapped signals. The proposed algorithm leverages dual-type images as deep learning input data, which is composed of spectrogram and signal images. The dual-type images have the features of both individual images, such as frequency change over time and information on amplitude and phase. We improve modulation classification performance by reflecting various features of a single signal. The simulation results show that the proposed algorithm has accurate classification performance compared to single-type input images, especially in analog modulation classification. © 2024 ieee.

关键词： signal modulation

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